The present invention relates to a method and a device for detecting the similarity between an unknown pattern and a standard pattern in a pattern recognition device.
In a character recognition device for recognizing printed characters such as numerals, alphabets, chinese characters, symbols etc., a similarity detection scheme is well-known.
That is, an unknown pattern to be recognized in segmented from digital patterns obtained by the scanning of a document, and there is then detected the similarity between the segmented unknown pattern and each of a plurality of standard patterns.
A category corresponding to a standard pattern having the largest similarity is selected as the recognition result of the unknown pattern.
However, in a case where the segmented unknown pattern and a standard pattern are displaced in position relative to each other due to some deformation of the unknown pattern, the similarity detected by shifting the positions of these patterns may be larger than that detected by a non-shifting of pattern positions.
Thus, in the prior art, the unknown pattern and the standard pattern are relatively shifted in position over a desired extend and the similarities in respective shifting conditions are detected, thereby determining the maximum value of the similarities as the similarity between the unknown pattern and the standard pattern.
However, in such a prior art recognition device, if both patterns are relatively shifted over a small extent, for example, by one picture cell in eight directions, it is impossible to recognize very accurately the unknown pattern. Thus, a larger shifting extent is desired. However, if both patterns are relatively shifted over a larger extent, for example, by two picture cells in eight directions, processing time for the similarity detection becomes longer or the hardware cost increases.